In large-scale AI systems, allocating scarce resources such as GPU compute time and bandwidth among multiple agents is a critical challenge. Conventional policies focus on efficiency metrics, potentially leading to dominance concentration that undermines system diversity and stability. We propose Computable Fair Division (CFD), a framework that reinterprets the Boltzmann-Softmax function not as a selection tool but as a probabilistic resource allocation mechanism, redefining the inverse temperature parameter $β$ as a computable control variable governing the efficiency-fairness balance. Static analysis reveals a Pareto frontier with a near-optimal Stability Corridor where total loss remains approximately constant across policy weights. In the dynamic setting, AHC++ (Adaptive Hard-Cap Controller++) updates $β$ in real time using the error between observed dominance and a policy-specified target as feedback. Simulations show that AHC++ suppresses extreme dominance concentration under exogenous shocks while tracking fairness targets without substantial throughput degradation. Scalability analysis confirms that a 100x increase in agents yields only approximately 5.5x increase in execution time. Code: https://github.com/entrofy-ai/computable-fairness

翻译：在大规模AI系统中，在多智能体间分配GPU计算时间、带宽等稀缺资源是一项关键挑战。传统策略侧重于效率指标，可能导致主导地位集中化，从而削弱系统的多样性与稳定性。我们提出可计算公平分配（CFD）框架，该框架将玻尔兹曼-softmax函数重新诠释为一种概率性资源分配机制（而非选择工具），并将逆温度参数β重新定义为可计算控制变量，用以调控效率-公平性平衡。静态分析揭示了帕累托前沿中存在近最优的稳定走廊，在该区域内策略权重变化时总损失保持近似恒定。在动态场景下，AHC++（自适应硬上限控制器++）利用观测的主导地位与策略指定目标之间的误差作为反馈，实时更新β参数。仿真结果表明，AHC++在跟踪公平性目标的同时，能够抑制外生冲击下的极端主导地位集中化，且不会显著降低吞吐量。可扩展性分析证实，智能体数量增加100倍时，执行时间仅增加约5.5倍。代码：https://github.com/entrofy-ai/computable-fairness